Pandemic perspectives: A scoping review of undergraduate nursing students' motivations in the age of COVID-19
Bibliographic record
Abstract
Background and objective: The COVID-19 pandemic intensified global nursing shortages, underscoring the urgency to understand the evolving motivations of undergraduate nursing students (UNS). While intrinsic and extrinsic factors are known to influence career choices, the pandemic's impact necessitates a closer examination of these motivations. This scoping review maps and explores primary research on the multifaceted motivations influencing UNS career decisions during and after the COVID-19 pandemic, providing a comprehensive overview of the factors shaping the future nursing workforce.Methods: Guided by Arksey and O'Malley's framework and PRISMA-ScR guidelines, a systematic search across five databases (CINAHL, PsycInfo, MEDLINE, Embase, Scopus) identified relevant studies published between January 2020 and January 2024. Rigorous screening and data extraction were followed by qualitative synthesis to identify key motivational themes.Results: Thirteen studies from diverse regions (Asia, Middle East, United States, Europe) revealed a complex interplay of intrinsic (e.g., personal fulfillment, altruism) and extrinsic (e.g., financial security, job prospects) motivations. The pandemic amplified concerns about occupational risks and work-life balance while highlighting the profession's societal value. Gender disparities emerged, with distinct motivations and barriers observed among male and female students.Conclusions: This review provides a nuanced understanding of UNS career motivations in the pandemic era, emphasizing the need for tailored recruitment and retention strategies that address both intrinsic aspirations and extrinsic concerns. By recognizing the multifaceted nature of motivations and their regional variations, stakeholders can foster a resilient and adaptable nursing workforce equipped to meet the challenges of the evolving healthcare landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".